Commenced in January 2007
Paper Count: 30855
Towards a Framework for Embedded Weight Comparison Algorithm with Business Intelligence in the Plantation Domain
Abstract:Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126379Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 820
 Poku, Kwasi. Small-scale palm oil processing in Africa. Vol. 148. Food & Agriculture Org., 2002.
 Gatto, M. (2015). Land-use dynamics, economic development, and institutional change in rural Communities-Evidence from the Indonesian oil palm sector (Doctoral dissertation, Niedersächsische Staats-und Universitäts bibliothek Göttingen).
 Kumawat, H., & Jain, P. (2014, July). In-Motion Weighing with Vehicle Data Collection System. In International Journal of Engineering Research and Technology (Vol. 3, No. 7 (July-2014)). ESRSA Publications.
 Patel, Hetal N., R. K. Jain, and Manjunath V. Joshi. "Fruit detection using improved multiple features based algorithm." International journal of computer applications 13.2 (2011): 1-5.
 LingHoak, O., YongChoon, H., & SengHeng, T. (2003). Automation of yield recording and yield mapping in oil palm plantations. Planter, 79(931), 643-659.
 Zheng, C. X., & Chen, J. (2013). Design on Structure and Function of Weighting Monitoring and Control System for Mine Railway Weighbridge.Coal Engineering, 4, 045.
 Markley, J., Raines, A., Langham, D. A., & Fleming, G. (2006). Harvest and transport integration-A Mackay sugar perspective. In Proceedings-Australian Society of Sugar Cane Technologists (Vol. 2006, p. 49). PK Editorial Services; 1999.
 Sivasothy, K. (2005). A new approach to plant-wide automation of palm oil mills. In Proceedings of the 2005 MPOB National Seminar. Paper 1.
 Taylor, K. (1984, January). Computerization of the laboratory at the Illovo mill. In Proc. S. Afr. Sug. Technol. Ass (Vol. 58, pp. 68-73).
 Bhero, E., & Hoffman, A. (2014). Optimizing Border-Post Cargo Clearance with Auto-ID Systems. Journal of Machine to Machine Communications,1(1), 17-30.
 Donough, C. R., Witt, C., & Fairhurst, T. H. (2010). Yield intensification in oil palm using BMP as a management tool. In International Conference on Oil Palm and the Environment.
 Nambiar, E. K. S., & Harwood, C. E. (2014). Productivity of acacia and eucalypt plantations in Southeast Asia. 1. Bio-physical determinants of production: opportunities and challenges. International Forestry Review,16(2), 225-248.
 Walsh, D., & Strandgard, M. (2014). Productivity and cost of harvesting a stemwood biomass product from integrated cut-to-length harvest operations in Australian Pinus radiata plantations. Biomass and Bioenergy, 66, 93-102.
 Bensaeed, O. M., et al. "Oil palm fruit grading using a hyperspectral device and machine learning algorithm." IOP conference series: Earth and environmental science. Vol. 20. No. 1. IOP Publishing, 2014.
 Liang, B. (2012, October). Research on System of Process Information Integrating and Monitoring for Production Process of Metallurgical Industry Based on Web. In Advanced Materials Research (Vol. 566, pp. 616-619).
 Sørensen, C. G., & Bochtis, D. D. (2010). Conceptual model of fleet management in agriculture. Biosystems Engineering, 105(1), 41-50.
 Kumar, A. (2002). A Review of Road Sector Reforms in Tanzania. Sub-Saharan Africa Transport Policy Program Discussion Paper, 2.
 Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.
 Hashim, N. M. Z., Mazlan, S. R., Aziz, M. A., Salleh, A., Ja’afar, A. S., & Mohamad, N. R. (2015). Agriculture Monitoring System: A Study. Jurnal Teknologi, 77(1).
 Babaria, K., Jacob, M. G., Mistry, V., Das, S., Iyer, K., & Biswas, A. Survey on Predictive analysis for formulating real time data in precision agriculture.
 Lee, E. (2000). What's ahead for embedded software? Computer, 33(9), 18-26.
 Wixom, B., & Watson, H. (2012). The BI-based organization. Organizational Applications of Business Intelligence Management: Emerging Trends, IGI Global, Hershey, 193-208.
 Devlin, B. (2010). Beyond business intelligence. Business Intelligence Journal, 15(2), 7-16.
 Gurjar, Y. S., & Rathore, V. S. (2013). Cloud business intelligence–is what business need today. International Journal of Recent Technology and Engineering, 1(6), 81-86.
 Gangadharan, G. R., & Swami, S. N. (2004, June). Business intelligence systems: design and implementation strategies. In Information Technology Interfaces, 2004. 26th International Conference on (pp. 139-144). IEEE.
 Wu, D. D., Chen, S. H., & Olson, D. L. (2014). Business intelligence in risk management: Some recent progresses. Information Sciences, 256, 1-7.
 Baars, H., & Kemper, H. G. (2008). Management support with structured and unstructured data—an integrated business intelligence framework. Information Systems Management, 25(2), 132-148.
 Azvine, B., Cui, Z., Nauck, D. D., & Majeed, B. (2006, June). Real time business intelligence for the adaptive enterprise. In E-Commerce Technology, 2006. The 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services, The 3rd IEEE International Conference on (pp. 29-29). IEEE.
 Hira, Swati, and P. S. Deshpande. "Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters."Procedia Computer Science 54 (2015): 431-439.
 Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey. "Business Intelligence and Analytics: From Big Data to Big Impact." MIS quarterly 36.4 (2012): 1165-1188.
 Kocher, Paul, et al. "Security as a new dimension in embedded system design." Proceedings of the 41st annual Design Automation Conference. ACM, 2004.
 Sangiovanni-Vincentelli, Alberto, and Marco Di Natale. "Embedded system design for automotive applications." IEEE Computer 40.10 (2007): 42-51.
 Amalathas, Sagaya, Antonija Mitrovic, and Saravanan Ravan. "Decision-Making Tutor: Providing on-the-job training for oil palm plantation managers." Research and Practice in Technology-Enhanced Learning 7.3 (2012): 131-152.
 Butler, M., Herlihy, P., & Keenan, P. B. (2005). Integrating information technology and operational research in the management of milk collection. Journal of Food Engineering, 70(3), 341-349.